DYNAMIC FILE MIGRATION IN DISTRIBUTED COMPUTER SYSTEMS

In a distributed computer system files are shared by both local users and remote users for query and update purposes. A user performing data processing activities tends to reference the same file for some time. When the referenced file is stored remotely, large amounts of communication traffic will be generated. For example, when a customer is making a travel plan, an airline reservation database might be accessed repeatedly by a remote operation site. The inquiries will probably all be made within the time of an ordinary telephone conversation. In many recent developments in distributed computer systems, file migration operations are incorporated into the procedures for processing remote file access requests. Using file migration operations a file may be duplicated or moved to the requesting site in order to reduce communication traffic. As a result, the system is faced with dynamic file placement decisions using a file migration policy. In particular, a file migration policy is expressed as the IF-THEN rules that specify the file migration operations to be implemented at each viable system state. Based on this policy, file migration operations are triggered when the specified conditions are satisfied, and thus dynamically respond to system needs. Because of the dynamic behaviors of systems, the problem of deriving effective file migration policies is extremely complex. An elaborate analysis is required. This paper studies the impact of file migration operations on system performance and develops automatic mechanisms for incorporating file migrations as part of system operations. The mechanisms include optimization models formulated in the form of Markov decision models for deriving optimal file migration policies at system design or redesign points, and heuristic rules to generate adaptive file migration decisions for individual file access requests. The trade-off between these two types of mechanisms is clearly that of performance levels versus implementation complexities. The optimization analysis not only generates the best possible solutions, but provides insight into the problem structure, whereas the rationale for developing heuristics is their simplicity in implementation and acceptable performance levels

Energy-aware Load Balancing Policies for the Cloud Ecosystem

The energy consumption of computer and communication systems does not scale linearly with the workload. A system uses a significant amount of energy even when idle or lightly loaded. A widely reported solution to resource management in large data centers is to concentrate the load on a subset of servers and, whenever possible, switch the rest of the servers to one of the possible sleep states. We propose a reformulation of the traditional concept of load balancing aiming to optimize the energy consumption of a large-scale system: {\it distribute the workload evenly to the smallest set of servers operating at an optimal energy level, while observing QoS constraints, such as the response time.} Our model applies to clustered systems; the model also requires that the demand for system resources to increase at a bounded rate in each reallocation interval. In this paper we report the VM migration costs for application scaling.Comment: 10 Page

A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure

Recent technology advancements in the areas of compute, storage and networking, along with the increased demand for organizations to cut costs while remaining responsive to increasing service demands have led to the growth in the adoption of cloud computing services. Cloud services provide the promise of improved agility, resiliency, scalability and a lowered Total Cost of Ownership (TCO). This research introduces a framework for minimizing cost and maximizing resource utilization by using an Integer Linear Programming (ILP) approach to optimize the assignment of workloads to servers on Amazon Web Services (AWS) cloud infrastructure. The model is based on the classical minimum-cost flow model, known as the assignment model.Comment: 2017 IEEE 10th International Conference on Cloud Computin

Admission Control and Scheduling for High-Performance WWW Servers

In this paper we examine a number of admission control and scheduling protocols for high-performance web servers based on a 2-phase policy for serving HTTP requests. The first "registration" phase involves establishing the TCP connection for the HTTP request and parsing/interpreting its arguments, whereas the second "service" phase involves the service/transmission of data in response to the HTTP request. By introducing a delay between these two phases, we show that the performance of a web server could be potentially improved through the adoption of a number of scheduling policies that optimize the utilization of various system components (e.g. memory cache and I/O). In addition, to its premise for improving the performance of a single web server, the delineation between the registration and service phases of an HTTP request may be useful for load balancing purposes on clusters of web servers. We are investigating the use of such a mechanism as part of the Commonwealth testbed being developed at Boston University