A Heuristic Algorithm for Resource Allocation/Reallocation Problem

This paper presents a 1-opt heuristic approach to solve resource allocation/reallocation problem which is known as 0/1 multichoice multidimensional knapsack problem (MMKP). The intercept matrix of the constraints is employed to find optimal or near-optimal solution of the MMKP. This heuristic approach is tested for 33 benchmark problems taken from OR library of sizes upto 7000, and the results have been compared with optimum solutions. Computational complexity is proved to be (2) of solving heuristically MMKP using this approach. The performance of our heuristic is compared with the best state-of-art heuristic algorithms with respect to the quality of the solutions found. The encouraging results especially for relatively large-size test problems indicate that this heuristic approach can successfully be used for finding good solutions for highly constrained NP-hard problems

Vector Bin Packing with Multiple-Choice

We consider a variant of bin packing called multiple-choice vector bin packing. In this problem we are given a set of items, where each item can be selected in one of several $D$-dimensional incarnations. We are also given $T$ bin types, each with its own cost and $D$-dimensional size. Our goal is to pack the items in a set of bins of minimum overall cost. The problem is motivated by scheduling in networks with guaranteed quality of service (QoS), but due to its general formulation it has many other applications as well. We present an approximation algorithm that is guaranteed to produce a solution whose cost is about $\ln D$ times the optimum. For the running time to be polynomial we require $D=O(1)$ and $T=O(\log n)$. This extends previous results for vector bin packing, in which each item has a single incarnation and there is only one bin type. To obtain our result we also present a PTAS for the multiple-choice version of multidimensional knapsack, where we are given only one bin and the goal is to pack a maximum weight set of (incarnations of) items in that bin

Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation

One of the challenges of deploying multitenant cloud-hosted services that are designed to use (or be integrated with) several components is how to implement the required degree of isolation between the components when there is a change in the workload. Achieving the highest degree of isolation implies deploying a component exclusively for one tenant; which leads to high resource consumption and running cost per component. A low degree of isolation allows sharing of resources which could possibly reduce cost, but with known limitations of performance and security interference. This paper presents a model-based algorithm together with four variants of a metaheuristic that can be used with it, to provide near-optimal solutions for deploying components of a cloud-hosted application in a way that guarantees multitenancy isolation. When the workload changes, the model based algorithm solves an open multiclass QN model to determine the average number of requests that can access the components and then uses a metaheuristic to provide near-optimal solutions for deploying the components. Performance evaluation showed that the obtained solutions had low variability and percent deviation when compared to the reference/optimal solution. We also provide recommendations and best practice guidelines for deploying components in a way that guarantees the required degree of isolation

A model for optimising the deployment of cloud-hosted application components for guaranteeing multitenancy isolation.

Tenants associated with a cloud-hosted application seek to reduce running costs and minimize resource consumption by sharing components and resources. However, despite the benefits, sharing resources can affect tenant’s access and overall performance if one tenant abruptly experiences a significant workload, particularly if the application fails to accommodate this sudden increase in workload. In cases where a there is a higher or varying degree of isolation between components, this issue can become severe. This paper aims to present novel solutions for deploying components of a cloud-hosted application with the purpose of guaranteeing the required degree of multitenancy isolation through a mathematical optimization model and metaheuristic algorithm. Research conducted through this paper demonstrates that, when compared, optimal solutions achieved through the model had low variability levels and percent deviation. This paper additionally provides areas of application of our optimization model as well as challenges and recommendations for deploying components associated with varying degrees of isolation

Mutable Protection Domains: Towards a Component-Based System for Dependable and Predictable Computing

The increasing complexity of software poses signicant challenges for real-time and embedded systems beyond those based purely on timeliness. With embedded sys-tems and applications running on everything from mobile phones, PDAs, to automobiles, aircraft and beyond, an emerging challenge is to ensure both the functional and tim-ing correctness of complex software. We argue that static analysis of software is insufcient to verify the safety of all possible control ow interactions. Likewise, a static sys-tem structure upon which software can be isolated in sepa-rate protection domains, thereby dening immutable bound-aries between system and application-level code, is too in-exible to the challenges faced by real-time applications with explicit timing requirements. This paper, therefore, in-vestigates a concept called mutable protection domains that supports the notion of hardware-adaptable isolation boundaries between software components. In this way, a system can be dynamically recongured to maximize soft-ware fault isolation, increasing dependability, while guar-anteeing various tasks are executed according to specic time constraints. Using a series of simulations on multi-dimensional, multiple-choice knapsack problems, we show how various heuristics compare in their ability to rapidly reorganize the fault isolation boundaries of a component-based system, to ensure resource constraints while simulta-neously maximizing isolation benet. Our ssh oneshot algorithm offers a promising approach to address system dynamics, including changing component invocation pat-terns, changing execution times, and mispredictions in iso-lation costs due to factors such as caching. This material is based upon work supported by the National Science Foundation under Grant Numbers 0615153 and 0720464. Any opinions, ndings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reect the views of the National Science Foundation.

Pavement Network Maintenance Optimization Considering Multidimensional Condition Data

A growing body of research seeks to optimize the selection and scheduling of maintenance, repair and rehabilitation activities for networks of sections of pavement. Such research typically relies on a composite condition index, a one-dimensional and often discrete measure of the overall structural health and/or serviceability of pavement. Pavement can suffer from a large number of related but distinct distresses. Difficulties associated with unobserved heterogeneity have hampered efforts to accurately model deterioration via composite condition indices. At the same time, optimization techniques used in pavement management have been shown both to be sensitive to deterioration model specification and to become computationally intractable as condition data increase. This research describes how approximate dynamic programming can be used to manage a large network of related sections of pavement each one of which may be plagued by a number of different distresses. Approximate dynamic programming mitigates the curse of dimensionality that has haunted distinct Markov decision problem formulations of the infrastructure management problem and limited their complexity. A computational study illustrates how the proposed approach leads to more sophisticated maintenance decision rules, which can be used to ensure the suggestions of pavement management systems more closely match engineering best practice

Architecting the deployment of cloud-hosted services for guaranteeing multitenancy isolation.

In recent years, software tools used for Global Software Development (GSD) processes (e.g., continuous integration, version control and bug tracking) are increasingly being deployed in the cloud to serve multiple users. Multitenancy is an important architectural property in cloud computing in which a single instance of an application is used to serve multiple users. There are two key challenges of implementing multitenancy: (i) ensuring isolation either between multiple tenants accessing the service or components designed (or integrated) with the service; and (ii) resolving trade-offs between varying degrees of isolation between tenants or components. The aim of this thesis is to investigate how to architect the deployment of cloud-hosted service while guaranteeing the required degree of multitenancy isolation. Existing approaches for architecting the deployment of cloud-hosted services to serve multiple users have paid little attention to evaluating the effect of the varying degrees of multitenancy isolation on the required performance, resource consumption and access privilege of tenants (or components). Approaches for isolating tenants (or components) are usually implemented at lower layers of the cloud stack and often apply to the entire system and not to individual tenants (or components). This thesis adopts a multimethod research strategy to providing a set of novel approaches for addressing these problems. Firstly, a taxonomy of deployment patterns and a general process, CLIP (CLoud-based Identification process for deployment Patterns) was developed for guiding architects in selecting applicable cloud deployment patterns (together with the supporting technologies) using the taxonomy for deploying services to the cloud. Secondly, an approach named COMITRE (COmponent-based approach to Multitenancy Isolation Through request RE-routing) was developed together with supporting algorithms and then applied to three case studies to empirically evaluate the varying degrees of isolation between tenants enabled by multitenancy patterns for three different cloud-hosted GSD processes, namely-continuous integration, version control, and bug tracking. After that, a synthesis of findings from the three case studies was carried out to provide an explanatory framework and new insights about varying degrees of multitenancy isolation. Thirdly, a model-based decision support system together with four variants of a metaheuristic solution was developed for solving the model to provide an optimal solution for deploying components of a cloud-hosted application with guarantees for multitenancy isolation. By creating and applying the taxonomy, it was learnt that most deployment patterns are related and can be implemented by combining with others, for example, in hybrid deployment scenarios to integrate data residing in multiple clouds. It has been argued that the shared component is better for reducing resource consumption while the dedicated component is better in avoiding performance interference. However, as the experimental results show, there are certain GSD processes where that might not necessarily be so, for example, in version control, where additional copies of the files are created in the repository, thus consuming more disk space. Over time, performance begins to degrade as more time is spent searching across many files on the disk. Extensive performance evaluation of the model-based decision support system showed that the optimal solutions obtained had low variability and percent deviation, and were produced with low computational effort when compared to a given target solution