Formation of the solar system essays

Formation of the solar system essays The solar system was thought to have formed with the collapse of a giant gas cloud. The giant gas cloud was thought to have been spherical in shape because like everything else in the universe, it was spinning. This was probably due to the fact that the gas it was made up with came from the stars which were spinning too. The collapse was thought to have been started by the inconsistent rise and fall of the density of the cloud, there came a point of time where there was enough material (like water, hydrogen, helium and interstellar dust) to cause the cloud to collapse. The cloud rotates faster and faster in order to conserve its angular momentum and according to the laws of physics, the faster an object spins, the smaller it becomes. As a result, the cloud begins to contract too. The cloud spins faster and faster and it spins so fast that a strong outward centrifugal force is caused and it is unable to contract any smaller. A dense disc of gas spinning around the central lump is also formed as it is simpler for matter to follow the gravitational pull along the axis of rotation (of the lump) rather than perpendicular to it as that is where the contrasting centrifugal force is supreme. As the gas cloud contracts and rotates faster, the temperature of the dust and gas particles increases until the cloud is hot and dense enough for nuclear reactions to begin and thereby giving birth to a star. When nuclear fusion takes place, the hydrogen atoms combine to produce helium, and this process gives out immense amount of energy. The spin of the sun is also reduced as the solar radiation from the newborn star will blow away all light gassy elements which is within approximately 6.667 AU (10^12m) of it, forming the solar nebula. As the molecules of elements such as carbon and silicon knock against each other, they stick and gradually form tiny lumps. This process is extremely slow owing to the fact that the atoms and molecules hardly ever b ...

The Debate on Capital Punishment in Canada Essay

The Debate on Capital Punishment in Canada - Essay Example e history of capital punishment in Canada dates back to 1749 when Peter Cartcel was hanged to death after he was convicted of the murder of one person and injuring two other people (Matthiassen, MÃ ¶ller and Forsman, 2003.). That act became the beginning of capital punishment in Canada and has remained in the law books of the country since then. But looking at the fact that the life of a person is the most expensive treasure that any one can possess, I oppose the capital punishment, which prescribes that those who take life must have their lives taken. From a legal perspective, am against capital punishment in Canada because people can be accused wrongly of the murder of other people and once death penalty is prescribed for such people, it will not be possible to restore their lives after they are latter found to be innocent. There remain several accounts in the legal system of Canada whereby people who have been convicted of murder cases have appealed their cases and own (SacKinnon, 1999). This means that any hasty decisions and actions that were taken to prescribe the capital punishment for these people would have deprived these people forever and there would have been no means of reversing their judgment. Meanwhile, the laws of the land demand that all people who are accused of cases have the right to appeal in higher courts of hearing (Mathiassen and Ahsberg, 2009,). Essentially, any person who is given the capital punishment and eventually executed will be denied that precious right to appeal. What is more, the laws are very silent on the number of years within which a person may appeal his or her judgment. Therefore, if people who are accused of murder are speared their lives, they will be in a better position to benefit from the law of appeal in many years to come so that they can have criminal charges leveled against them reversed. On humanitarian grounds, there can virtually be no argument that supports the killing of human life as the human life is very

Law for Business Essay Example | Topics and Well Written Essays - 1750 words

Law for Business - Essay Example Either the acceptance of an offer may be a statement of agreement, or, if the offer invites acceptance in this way, a performance of an act requested in the terms of the offer. It is important to note that acceptance is the final and unqualified acceptance and it must be according to the types and the requirements of the offer hence acceptance must exactly match the offer. Other forms of acceptance of an offer may be spoken, written, by action and an acceptance is never acceptance when there is silence and assumption. For example, if one tells a neighbor kid that if the kid mows the offeror’s lawn, the offeror will pay $50.00, and the kid does actually mow the lawn. The act of mowing constitutes the manifestation of the kid’s assent hence constituting an acceptance given the offer presented and the terms upon which it is presented. For a contract based on offer and acceptance to be binding and enforceable by law, the terms must be capable of determination in the same co urts of law in a way that it is clear that the parties assent was given to the same terms of agreement. The terms must be in the position of being able to manifest themselves or be determined determined objectively. They may be written, or sometimes oral, although some kinds of contracts require writing as evidence of the agreement to be enforced in the courts of law. What is an offer? In an offer the offerer decides to make an offer and goes further vto communicate the offer. The offeree is then left with the responsibility of either accepting or rejecting the offer of which they have to communicate the acceptance in the case they decide to accept the offer with the terms around it Fisher v Bell [1961] 1 QB 394. The offerer then decides to receive the acceptance and the contract remains binding. An offer therefore can be defined as definite promise to be bound upon some specific terms, making it be a proposition by one party to another party on fixed terms that are fixed or in a po sition of being fixed. This is with the intention that it will be binding when accepted by the offeree in either of the forms prescribed for acceptance. In fact, a definite offer does not need to be made to a specific person, this is because it can be made to a particular class of persons or the world in general and it would remain binding if all the requirements were adhered. An  offer  is actually an expression of willingness to contract on certain terms, made with the intention that it shall become binding as soon as the person to whom it is addressed, the â€Å"offeree† [G.H. Tretel, The Law of Contract, 10th edn, p.8], accepts it. The â€Å"expression† referred to in the definition of an offer may take different forms, such as a letter, newspaper, fax, email and even conduct, as long as it communicates the basis on which the offeror is prepared to contract. The â€Å"intention† referred to in the definition is objectively judged and only determinable by courts. The English case of Smith v. Hughes (1871) LR 6 QB 597 emphasizes that the important thing is not the party’s real intentions but; how a reasonable person would view the situation making. This makes an offer not to be so specific to an individual. This is mainly due to common sense as each party would not wish to breach his side of the contract, if it would make him or her culpable to damages, it would especially be contrary to the principle of certainty and clarity in

Improving the Performance of Overbooking

Improving the Performance of Overbooking Improving the Performance of Overbooking by Application Collocate Using Affinity Function ABSTRACT: One of the main features provided by clouds is elasticity, which allows users to dynamically adjust resource allocations depending on their current needs. Overbooking describes resource management in any manner where the total available capacity is less than the theoretical maximal requested capacity. This is a well-known technique to manage scarce and valuable resources that has been applied in various fields since long ago. The main challenge is how to decide the appropriate level of overbooking that can be achieved without impacting the performance of the cloud services. This paper focuses on utilizing the Overbooking framework that performs admission control decisions based on fuzzy logic risk assessments of each incoming service request. This paper utilizes the collocation function (affinity) to define the similarity between applications. The similar applications are then collocated for better resource scheduling. I. INTRODUCTION Scheduling, or placement, of services is the process of deciding where services should be hosted. Scheduling is a part of the service deployment process and can take place both externally to the cloud, i.e., deciding on which cloud provide the service should be hosted, and internally, i.e., deciding which PM in a datacenter a VM should be run on. For external placement, the decision on where to host a service can be taken either by the owner of the service, or a third-party brokering service. In the first case, the service owner maintains a catalog of cloud providers and performs the negotiation with them for terms and costs of hosting the service. In the later case, the brokering service takes responsibility for both discovery of cloud providers and the negotiation process. Regarding internal placement, the decision of which PMs in the datacenter a service should be hosted by is taken when the service is admitted into the infrastructure. Depending on criteria such as the current loa d of the PMs, the size of the service and any affinity or anti-affinity constraints [23], i.e., rules for co-location of service components, one or more PMs are selected to run the VMs that constitute the service. Figure 1 illustrates a scenario with new services of different sizes (small, medium, and large) arriving into a datacenter where a number of services are already running. Figure 1: Scheduling in VMs Overload can happen in an oversubscribed cloud. Conceptually, there are two steps for handling overload, namely, detection and mitigation, as shown in Figure 2. Figure 2: Oversubscription view A physical machine has CPU, memory, disk, and network resources. Overload on an oversubscribed host can manifest for each of these resources. When there is memory overload, the hyper visor swaps pages from its physical memory to disk to make room for new memory allocations requested by VMs (Virtual Machines). The swapping process increases disk read and write traffic and latency, causing the programs to thrash. Similarly, when there is CPU overload, VMs and the monitoring agents running with VMs may not get a chance to run, thereby increasing the number of processes waiting in the VMs CPU run queue. Consequently, any monitoring agents running inside the VM also may not get a chance to run, rendering inaccurate the cloud providers view of VMs. Disk overload in shared SAN storage environment can increase the network traffic, where as in local storage it can degrade the performance of applications running in VMs. Lastly, network overload may result in an under utilization of CPU, disk, and memory resources, rendering ineffective any gains from oversubscription. Overload can be detected by applications running on top of VMs, or by the physical host running the VMs. Each approach has its pros and cons. The applications know their performance best, so when they cannot obtain the provisioned resources of a VM, it is an indication of overload. The applications running on VMs can then funnel this information to the management infrastructure of cloud. However, this approach requires modification of applications. In the overload detection within physical host, the host can infer overload by monitoring CPU, disk, memory, and network utilizations of each VM process, and by monitoring the usage of each of its resources. The benefit of this approach is that no modification to the applications running on VMs is required. However, overload detection may not be fully accurate. II. RELATED WORK The scheduling of services in a datacenter is often performed with respect to some high-level goal [36], like reducing energy consumption, increasing utilization [37] and performance [27] or maximizing revenue [17, 38]. However, during operation of the datacenter, the initial placement of a service might no longer be suitable, due to variations in application and PM load. Events like arrival of new services, existing services being shut down or services being migrated out of the datacenter can also affect the quality of the initial placement. To avoid drifting too far from an optimal placement, thus reducing efficiency and utilization of the datacenter, scheduling should be performed repeatedly during operation. Information from monitoring probes [23], and events such as timers, arrival of new services, or startup and shutdown of PMs can be used to determine when to update the mapping between VMs and PMs. Scheduling of VMs can be considered as a multi-dimensional type of the Bin Packing [10] problem, where VMs with varying CPU, I/O, and memory requirements are placed on PMs in such a way that resource utilization and/or other objectives are maximized. The problem can be addressed, e.g., by using integer linear programming [52] or by performing an exhaustive search of all possible solutions. However, as the problem is complex and the number of possible solutions grow rapidly with the amount of PMs and VMs, such approaches can be both time and resource consuming. A more resource efficient, and faster, way is the use of greedy approaches like the First-Fit algorithm that places a VM on the first available PM that can accommodate it. However, such approximation algorithms do not normally generate optimal solutions. All in all, approaches to solving the scheduling problem often lead to a trade-o↠µ between the time to find a solution and the quality of the solution found. Hosting a ser vice in the cloud comes at a cost, as most cloud providers are driven by economical incentives. However, the service workload and the available capacity in a datacenter can vary heavily over time, e.g., cyclic during the week but also more randomly [5]. It is therefore beneficial for providers to be able to dynamically adjust prices over time to match the variation in supply and demand. Cloud providers typically offer a wide variety of compute instances, differing in the speed and number of CPUs available to the virtual machine, the type of local storage system used (e.g. single hard disk, disk array, SSD storage), whether the virtual machine may be sharing physical resources with other virtual machines (possibly belonging to different users), the amount of RAM, network bandwidth, etc. In addition, the user must decide how many instances of each type to provision. In the ideal case, more nodes means faster execution, but issues of heterogeneity, performance unpredictability, network overhead, and data skew mean that the actual benefit of utilizing more instances can be less than expected, leading to a higher cost per work unit. These issues also mean that not all the provisioned resources may be optimally used for the duration of the application. Workload skew may mean that some of the provisioned resources are (partially) idle and therefore do no contribute to the performance during those periods, but still contribute to cost. Provisioning larger or higher performance instances is similarly not always able to yield a proportional benefit. Because of these factors, it can be very difficult for a user to translate their performance requirements or objectives into concrete resource specifications for the cloud. There have been several works that attempt to bridge this gap, which mostly focus on VM allocation [HDB11, VCC11a, FBK+12, WBPR12] and d etermining good configuration parameters [KPP09, JCR11, HDB11]. Some more recent work also considers shared resources such as network or data storage [JBC+12], which is especially relevant in multi-tenant scenarios. Other approaches consider the provider side of things, because it can be equally difficult for a provider to determine how to optimally service resource requests [RBG12]. Resource provisioning is complicated further because performance in the cloud is not always predictable, and known to vary even among seemingly identical instances [SDQR10, LYKZ10]. There have been attempts to address this by extending resource provisioning to include requirement specifications for things such as network performance rather than just the number and type of VMs in an attempt to make the performance more predictable [GAW09, GLW+10, BCKR11, SSGW11]. Others try to explicitly exploit this variance to improve application performance [FJV+12]. Accurate provisioning based on application requirements also requires the ability to understand and predict application performance. There are a number of approaches towards estimating performance: some are based on simulation [Apad, WBPG09], while others use information based on workload statistics derived from debug execution [GCF+10, MBG10] or profiling sample data [TC11, HDB11]. Most of these approaches still have limited accuracy, especially when it comes to I/O performance. Cloud platforms run a wide array of heterogeneous workloads which further complicates this issue [RTG+12]. Related to provisioning is elasticity, which means that it is not always necessary to determine the optimal resource allocation beforehand, since it is possible to dynamically acquire or release resources during execution based on observed performance. This suffers from many of the same problems as provisioning, as it can be difficult to accurately estimate the impact of changing the resources at runtime, and therefore to decide when to acquire or release resources, and which ones. Exploiting elasticity is also further complicated when workloads are statically divided into tasks, as it is not always possible to preempt those tasks [ADR+12]. Some approaches for improving workload elasticity depend on the characteristics of certain workloads [ZBSS+10, AAK+11, CZB11], but these characteristics may not generally apply. It is therefore clear that it can be very difficult to decide, f or either the user or the provider, how to optimally provision resources and to ensure that those resources that are provisioned are utilized fully. Their is a very active interest in improving this situation, and the approaches proposed in this thesis similarly aim to improve provisioning and elasticity by mitigating common causes of inefficient resource utilization. III. PROPOSED OVERBOOKING METHOD The proposed model utilizes the concept of overbooking introduced in [1] and schedules the services using the collocation function. 3.1 Overbooking: The Overbooking is to exploit overestimation of required job execution time. The main notion of overbooking is to schedule more number of additional jobs. Overbooking strategy used in economic model can improve system utilization rate and occupancy. In overbooking strategy every job is associated with release time and finishing deadline, as shown in Fig 3. Here successful execution will be given with fee and penalty for violating the deadline. Figure 3: Strategy of Overbooking Data centers can also take advantage of those characteristics to accept more VMs than the number of physical resources the data center allows. This is known as resource overbooking or resource over commitment. More formally, overbooking describes resource management in any manner where the total available capacity is less than the theoretical maximal requested capacity. This is a well-known technique to manage scarce and valuable resources that has been applied in various fields since long ago. Figure 4: Overview of Overbooking The above Figure shows a conceptual overview of cloud overbooking, depicting how two virtual machines (gray boxes) running one application each (red boxes) can be collocated together inside the same physical resource (Server 1) without (noticeable) performance degradation. The overall components of the proposed system are depicted in figure 5. Figure 5: Components of the proposed model The complete process of the proposed model is explained below: The user requests the scheduler for the services The scheduler first verifies the AC and then calculates the Risk of that service. Then already a running service is scheduling then the request is stored in a queue. The process of FIFO is used to schedule the tasks. To complete the scheduling the collocation function keeps the intermediate data nodes side by side and based on the resource provision capacity the node is selected. If the first node doesn’t have the capacity to complete the task then the collocation searches the next node until the capacity node is found. The Admission Control (AC) module is the cornerstone in the overbooking framework. It decides whether a new cloud application should be accepted or not, by taking into accounts the current and predicted status of the system and by assessing the long term impact, weighting improved utilization against the risk of performance degradation. To make this assessment, the AC needs the information provided by the Knowledge DB, regarding predicted data center status and, if available, predicted application behavior. The Knowledge DB (KOB) module measures and profiles the different applications’ behavior, as well as the resources’ status over time. This module gathers information regarding CPU, memory, and I/O utilization of both virtual and physical resources. The KOB module has a plug-in architectural model that can use existing infrastructure monitoring tools, as well as shell scripts. These are interfaced with a wrapper that stores information in the KOB. The Smart Overbooking Scheduler (SOS) allocates both the new services accepted by the AC and the extra VMs added to deployed services by scale-up, also de-allocating the ones that are not needed. Basically, the SOS module selects the best node and core(s) to allocate the new VMs based on the established policies. These decisions have to be carefully planned, especially when performing resource overbooking, as physical servers have limited CPU, memory, and I/O capabilities. The risk assessment module provides the Admission Control with the information needed to take the final decision of accepting or rejecting the service request, as a new request is only admitted if the final risk is bellow a pre-defined level (risk threshold). The inputs for this risk assessment module are: Req CPU, memory, and I/O capacity required by the new incoming service. UnReq The difference between total data center capacity and the capacity requested by all running services. Free the difference between total data center capacity and the capacity used by all running services. Calculating the risk of admitting a new service includes many uncertainties. Furthermore, choosing an acceptable risk threshold has an impact on data center utilization and performance. High thresholds result in higher utilization but the expense of exposing the system to performance degradation, whilst using lower values leads to lower but safer resource utilization. The main aim of this system is to use the affinity function that aid the scheduling system to decide which applications are to be placed side by side (collocate). Affinity function utilizes the threshold properties for defining the similarity between the applications. The similar applications are then collocated for better resource scheduling. IV. ANALYSIS: The proposed system is tested for time taken to search and schedule the resources using the collocation the proposed system is compared with the system developed in [1]. The system in [1] doesn’t contain a collocation function so the scheduling process takes more time compared to the existing system. The comparison results are depicted in figure 6. Figure 6: Time taken to Complete Scheduling The graphs clearly depict that the modified (Proposed overbooking takes equal time to complete the scheduling irrespective of the requests.

Ethics and Decision Making Essay -- Ethical Behavior Workplace Essays

Ethics and Decision Making Ethics can have a big influence on decision-making in the workplace. Ethical behavior in the workplace is behavior that is accepted as morally â€Å"right,† rather than â€Å"wrong.† (Organizational Behavior). Unethical behavior can be considered illegal, or merely against the norms of society. Employees encounter ethical decisions every day in the workplace, whether they realize it or not. The stock boy must make a decision on whether it is right to steal merchandise. The auto mechanic must make a decision on what is a fair price to charge a gullible customer. The CEO must decide how to use all the power he or she possesses. There are many definitions of ethical behavior, and different people will view the same situation differently depending on their ethical thought process. The utilitarian view of ethical thinking states that ethical behavior is when the greatest good is done for the greatest number of people. This usually means, in a business sense, that one department, program, or factory must be shut down to help the company function more efficiently or be more financially stable. The individualism view is just that, decisions must be based on what is best for the individual’s interests in the long run. The moral rights view suggests that the basic rights of citizens should be respected. The rights of fair treatment, privacy, and freedom of speech are thought of as such moral rights. The justice view emphasizes fair and imparti...

Abolitionism and the Uplift Movement

Justin Schenck March 7, 2013 Abolitionism Prof. Price The Uplift Movement and Origins of the â€Å"Black† Church In the late 18th century after the end of the revolution many new opportunities and hopefully thinking caused African Americans to start fighting for equality through the Uplift movement. This was an era where the Great Awakening and Enlightenment were becoming much more popular nationwide. Secret abolition societies and organizations were sprouting up all across the new Republic.These free thinkers and new anti-slavery organizations called for the need of a place to gather without racial discrimination and where the members could feel comfortable. I believe that the solution for this problem was the development of African American churches where racial segregation was not present and the black community along with white activist could gather comfortably for worship, opportunity, social/scholastic education, and held as a place for various activist meetings.The firs t of the churches was founded by two former slaves, Richard Allen and Absalom Jones. The African Methodist Episcopal Church was established in 1792. These churches created many leaders in the fight for racial equality and the abolishment of slavery. The churches were open to both free blacks and slaves. White slave owners would bring their slaves to church with them and justified slavery by saying that the church is teaching them Christianity even though they experienced very little access to a quality service.These new â€Å"black† churches gave African Americans the chance to decipher the bible in their own way and spiritual traditions which have carried on still to this day. Along with these new traditions created leadership roles in the church which were nonexistent in the mainstream churches. Not only were these churches a place for comfortable worship but also served as a â€Å"headquarters† for meetings among the anti-slavery and racial equality groups. Without these places for opportunities like jobs, petition rives, activist meetings, and schools the racial discrimination wouldn’t have gotten any better any faster. The schools created the knowledge to understand how to make it as a free person in the white society. Freed blacks were becoming much more literate which helped them learn how to fight for equality along with the religious side of equal rights for all men. Of course there were struggles along the way. The churches suffered violence from white denominations but the â€Å"black† churches would not fail under strong leadership.These preachers encouraged economic growth through community member’s donations and drives. This economic growth funded the schools and literacy programs. Also, Black leaders began to focus on politics and setting political goals. This was a time of growth all across the board and was the foundation of building a strong community, economic growth, and possibly most of all creating oppor tunity for leadership that had not been seen before.

Prom Date Monologue - Comedic Monologue for Male Actors

This comedic monologue is taken from Promedy, a teen-age comedy about Senior Prom, written by Wade Bradford. Dante is a rather goofy, overly dramatic vice-president of the Student Body. During an ASB meeting, in front of his fellow classmates, he decides to ask Kay, the text-messaging obsessed cheerleader, to be his prom date. Dante:Kay, I know that youre one of the most popular girls in school, and that you scarcely know how to pronounce my name, or use proper grammar. But Ive had a crush on you since the days when you would ignore me in kindergarten all the way to this afternoon when you ignored me in the cafeteria lunch line. Some might say ours is a misunderstood romance. I remember in second grade, I wrote you a note in Mrs. Souplangers class. It said, Do you like me? circle yes or no. My friends said you tore up the note in disgust. But I knew you were ripping up confetti to celebrate our newfound love. I know that you have been pretending to ignore me all these years because secretly, deep down, you know that you like me as much as I like you, probably even more. Well, you dont have to be scared and shy any more. Im going to make both of our wishes come true. Will you go to the prom with me?(Dante reacts to her rejection.)Should I take the rolling of your eyes as a yes? Learn more about the play and its availability.