Load Balancing in Cloud Computing: A Survey on Popular Techniques and Comparative Analysis

Cloud Computing is universally accepted as the most intensifying field in web technologies today. With the increasing popularity of the cloud, popular website2019;s servers are getting overloaded with high request load by users. One of the main challenges in cloud computing is Load Balancing on servers. Load balancing is the procedure of sharing the load between multiple processors in a distributed environment to minimize the turnaround time taken by the servers to cater service requests and make better utilization of the available resources. It greatly helps in scenarios where there is misbalance of workload on the servers as some machines may get heavily loaded while others remain under-loaded or idle. Load balancing methods make sure that every VM or server in the network holds workload equilibrium and load as per their capacity at any instance of time. Static and Dynamic load balancing are main techniques for balancing load on servers. This paper presents a brief discussion on different load balancing schemes and comparison between prime techniques

PVW: Designing Virtual World Server Infrastructure

This paper presents a high level overview of PVW (Partitioned Virtual Worlds), a distributed system architecture for the management of virtual worlds. PVW is designed to support arbitrarily large and complex virtual worlds while accommodating dynamic and highly variable user population and content distribution density. The PVW approach enables the task of simulating and managing the virtual world to be distributed over many servers by spatially partitioning the environment into a hierarchical structure. This structure is useful both for balancing the simulation load across many nodes, as well as features such as geometric simplification and distribution of dynamic content

Minimization of cloud task execution length with workload prediction errors

In cloud systems, it is non-trivial to optimize task’s execution performance under user’s affordable budget, especially with possible workload prediction errors. Based on an optimal algorithm that can minimize cloud task’s execution length with predicted workload and budget, we theoretically derive the upper bound of the task execution length by taking into account the possible workload prediction errors. With such a state-of-the-art bound, the worst-case performance of a task execution with a certain workload prediction errors is predictable. On the other hand, we build a close-to-practice cloud prototype over a real cluster environment deployed with 56 virtual machines, and evaluate our solution with different resource contention degrees. Experiments show that task execution lengths under our solution with estimates of worst-case performance are close to their theoretical ideal values, in both non-competitive situation with adequate resources and the competitive situation with a certain limited available resources. We also observe a fair treatment on the resource allocation among all tasks.published_or_final_versio

Taking It With You: Platform Barriers to Entry and the Limits of Data Portability

Policymakers are faced with a vexing problem: how to increase competition in a tech sector dominated by a few giants. One answer proposed and adopted by regulators in the United States and abroad is to require large platforms to allow consumers to move their data from one platform to another, an approach known as data portability. Facebook, Google, Apple, and other major tech companies have enthusiastically supported data portability through their own technical and political initiatives. Today, data portability has taken hold as one of the go-to solutions to address the tech industry’s competition concerns. This Article argues that despite the regulatory and industry alliance around data portability, today’s public and private data portability efforts are unlikely to meaningfully improve competition. This is because current portability efforts focus solely on mitigating switching costs, ignoring other barriers to entry that may preclude new platforms from entering the market. The technical implementations of data portability encouraged by existing regulation—namely one-off exports and API interoperability—address switching costs but not the barriers of network effects, unique data access, and economies of scale. This Article proposes a new approach to better alleviate these other barriers called collective portability, which would allow groups of users to coordinate to transfer data they share to a new platform, all at once. Although not a panacea, collective portability would provide a meaningful alternative to existing approaches while avoiding both the privacy/competitive utility trade off of one-off exports and the hard-to regulate power dynamics of APIs

Dynamic computation migration in distributed shared memory systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Vita.Includes bibliographical references (p. 123-131).by Wilson Cheng-Yi Hsieh.Ph.D

Elastic Resource Management in Distributed Clouds

The ubiquitous nature of computing devices and their increasing reliance on remote resources have driven and shaped public cloud platforms into unprecedented large-scale, distributed data centers. Concurrently, a plethora of cloud-based applications are experiencing multi-dimensional workload dynamics---workload volumes that vary along both time and space axes and with higher frequency. The interplay of diverse workload characteristics and distributed clouds raises several key challenges for efficiently and dynamically managing server resources. First, current cloud platforms impose certain restrictions that might hinder some resource management tasks. Second, an application-agnostic approach might not entail appropriate performance goals, therefore, requires numerous specific methods. Third, provisioning resources outside LAN boundary might incur huge delay which would impact the desired agility. In this dissertation, I investigate the above challenges and present the design of automated systems that manage resources for various applications in distributed clouds. The intermediate goal of these automated systems is to fully exploit potential benefits such as reduced network latency offered by increasingly distributed server resources. The ultimate goal is to improve end-to-end user response time with novel resource management approaches, within a certain cost budget. Centered around these two goals, I first investigate how to optimize the location and performance of virtual machines in distributed clouds. I use virtual desktops, mostly serving a single user, as an example use case for developing a black-box approach that ranks virtual machines based on their dynamic latency requirements. Those with high latency sensitivities have a higher priority of being placed or migrated to a cloud location closest to their users. Next, I relax the assumption of well-provisioned virtual machines and look at how to provision enough resources for applications that exhibit both temporal and spatial workload fluctuations. I propose an application-agnostic queueing model that captures the resource utilization and server response time. Building upon this model, I present a geo-elastic provisioning approach---referred as geo-elasticity---for replicable multi-tier applications that can spin up an appropriate amount of server resources in any cloud locations. Last, I explore the benefits of providing geo-elasticity for database clouds, a popular platform for hosting application backends. Performing geo-elastic provisioning for backend database servers entails several challenges that are specific to database workload, and therefore requires tailored solutions. In addition, cloud platforms offer resources at various prices for different locations. Towards this end, I propose a cost-aware geo-elasticity that combines a regression-based workload model and a queueing network capacity model for database clouds. In summary, hosting a diverse set of applications in an increasingly distributed cloud makes it interesting and necessary to develop new, efficient and dynamic resource management approaches