Graeme Gange

SLA-Based Resource Scheduling for Big Data Analytics as a Service in Cloud Computing Environments

Data analytics plays a significant role in gaining insight of big data that can benefit in decision making and problem solving for various application domains such as science, engineering, and commerce. Cloud computing is a suitable platform for Big Data Analytic Applications (BDAAs) that can greatly reduce application cost by elastically provisioning resources based on user requirements and in a pay as you go model. BDAAs are typically catered for specific domains and are usually expensive. Moreover, it is difficult to provision resources for BDAAs with fluctuating resource requirements and reduce the resource cost. As a result, BDAAs are mostly used by large enterprises. Therefore, it is necessary to have a general Analytics as a Service (AaaS) platform that can provision BDAAs to users in various domains as consumable services in an easy to use way and at lower price. To support the AaaS platform, our research focuses on efficiently scheduling Cloud resources for BDAAs to satisfy Quality of Service (QoS) requirements of budget and deadline for data analytic requests and maximize profit for the AaaS platform. We propose an admission control and resource scheduling algorithm, which not only satisfies QoS requirements of requests as guaranteed in Service Level Agreements (SLAs), but also increases the profit for AaaS providers by offering a cost-effective resource scheduling solution. We propose the architecture and models for the AaaS platform and conduct experiments to evaluate the proposed algorithm. Results show the efficiency of the algorithm in SLA guarantee, profit enhancement, and cost saving.

Abstract Interpretation over Non-lattice Abstract Domains

The classical theoretical framework for static analysis of programs is abstract interpretation. Much of the power and elegance of that framework rests on the assumption that an abstract domain is a lattice. Nonetheless, and for good reason, the literature on program analysis provides many examples of non-lattice domains, including non-convex numeric domains. The lack of domain structure, however, has negative consequences, both for the precision of program analysis and for the termination of standard Kleene iteration. In this paper we explore these consequences and present general remedies.

Failure tabled constraint logic programming by interpolation

We present a new execution strategy for constraint logic programs called Failure Tabled CLP. Similarly to Tabled CLP our strategy records certain derivations in order to prune further derivations. However, our method only learns from failed derivations. This allows us to compute interpolants rather than constraint projection for generation of reuse conditions. As a result, our technique can be used where projection is too expensive or does not exist. Our experiments indicate that Failure Tabling can speed up the execution of programs with many redundant failed derivations as well as achieve termination in the presence of infinite executions.

Optimal k-level planarization and crossing minimization

An important step in laying out hierarchical network diagrams is to order the nodes on each level. The usual approach is to minimize the number of edge crossings. This problem is NP-hard even for two layers when the first layer is fixed. Hence, in practice crossing minimization is performed using heuristics. Another suggested approach is to maximize the planar subgraph, i.e. find the least number of edges to delete to make the graph planar. Again this is performed using heuristics since minimal edge deletion for planarity is NP-hard.We show that using modern SAT and MIP solving approaches we can find optimal orderings for minimal crossing or minimal edge deletion for planarization on reasonably sized graphs. These exact approaches provide a benchmark for measuring quality of heuristic crossing minimization and planarization algorithms. Furthermore, we can straightforwardly extend our approach to minimize crossings followed by maximizing planar subgraph or vice versa; these hybrid approaches produce noticeably better layout then either crossing minimization or planarization alone.

Unbounded model-checking with interpolation for regular language constraints

We present a decision procedure for the problem of, given a set of regular expressions R1, …, Rn, determining whether R=R1∩⋯∩Rn is empty. Our solver, revenant, finitely unrolls automata for R1, …, Rn, encoding each as a set of propositional constraints. If a SAT solver determines satisfiability then R is non-empty. Otherwise our solver uses unbounded model checking techniques to extract an interpolant from the bounded proof. This interpolant serves as an overapproximation of R. If the solver reaches a fixed-point with the constraints remaining unsatisfiable, it has proven R to be empty. Otherwise, it increases the unrolling depth and repeats. We compare revenant with other state-of-the-art string solvers. Evaluation suggests that it behaves better for constraints that express the intersection of sets of regular languages, a case of interest in the context of verification.

MDD propagators with explanation

Multi-valued decision diagrams (MDDs) are a convenient approach to representing many kinds of constraints including table constraints, regular constraints, complex set and multiset constraints, as well as ad-hoc problem specific constraints. This paper introduces an incremental propagation algorithm for MDDs, and explores several methods for incorporating explanations with MDD-based propagators. We demonstrate that these techniques can provide significantly improved performance when solving a variety of problems.

Fast set bounds propagation using a BDD-SAT hybrid

Binary Decision Diagram (BDD) based set bounds propagation is a powerful approach to solving set-constraint satisfaction problems. However, prior BDD based techniques incur the significant overhead of constructing and manipulating graphs during search. We present a set-constraint solver which combines BDD-based set-bounds propagators with the learning abilities of a modern SAT solver. Together with a number of improvements beyond the basic algorithm, this solver is highly competitive with existing propagation based set constraint solvers.

Exploiting Sparsity in Difference-Bound Matrices

Relational numeric abstract domains are very important in program analysis. Common domains, such as Zones and Octagons, are usually conceptualised with weighted digraphs and implemented using difference-bound matrices (DBMs). Unfortunately, though conceptually simple, direct implementations of graph-based domains tend to perform poorly in practice, and are impractical for analyzing large code-bases. We propose new DBM algorithms that exploit sparsity and closed operands. In particular, a new representation which we call split normal form reduces graph density on typical abstract states. We compare the resulting implementation with several existing DBM-based abstract domains, and show that we can substantially reduce the time to perform full DBM analysis, without sacrificing precision.

High-Quality Ultra-Compact Grid Layout of Grouped Networks

Prior research into network layout has focused on fast heuristic techniques for layout of large networks, or complex multi-stage pipelines for higher quality layout of small graphs. Improvements to these pipeline techniques, especially for orthogonal-style layout, are difficult and practical results have been slight in recent years. Yet, as discussed in this paper, there remain significant issues in the quality of the layouts produced by these techniques, even for quite small networks. This is especially true when layout with additional grouping constraints is required. The first contribution of this paper is to investigate an ultra-compact, grid-like network layout aesthetic that is motivated by the grid arrangements that are used almost universally by designers in typographical layout. Since the time when these heuristic and pipeline-based graph-layout methods were conceived, generic technologies (MIP, CP and SAT) for solving combinatorial and mixed-integer optimization problems have improved massively. The second contribution of this paper is to reassess whether these techniques can be used for high-quality layout of small graphs. While they are fast enough for graphs of up to 50 nodes we found these methods do not scale up. Our third contribution is a large-neighborhood search meta-heuristic approach that is scalable to larger networks.

Synthesizing Optimal Switching Lattices

The use of nanoscale technologies to create electronic devices has revived interest in the use of regular structures for defining complex logic functions. One such structure is the switching lattice, a two-dimensional lattice of four-terminal switches. We show how to directly construct switching lattices of polynomial size from arbitrary logic functions; we also show how to synthesize minimal-sized lattices by translating the problem to the satisfiability problem for a restricted class of quantified Boolean formulas. The synthesis method is an anytime algorithm that uses modern SAT solving technology and dichotomic search. It improves considerably on an earlier proposal for creating switching lattices for arbitrary logic functions.