The prevalence of disaggregated storage in public clouds has led to increased latency in modern OLAP cloud databases, particularly when handling ad-hoc and highly-selective queries on large objects. To address this, cloud databases have adopted computation pushdown, executing query predicates closer to the storage layer. However, existing pushdown solutions are inefficient in erasure-coded storage. Cloud storage employs erasure coding that partitions analytics file objects into fixed-sized blocks and distributes them across storage nodes. Consequently, when a specific part of the object is queried, the storage system must reassemble the object across nodes, incurring significant network latency.

In recent years, emerging storage hardware technologies have focused on divergent goals: better performance or lower cost-per-bit. Correspondingly, data systems that employ these technologies are typically optimized either to be fast (but expensive) or cheap (but slow). We take a different approach: by architecting a storage engine to natively utilize two tiers of fast and low-cost storage technologies, we can achieve a Pareto efficient balance between performance and cost-per-bit.

The advent of tiny, inexpensive, low-power, autonomously functioning wireless sensors, has paved a way for high quality research, by posing unique challenges to researchers. Energy, cost and size of such sensors limit their communication and computation capabilities and thus robust and highly energy efficient algorithms need to be incorporated at every layer of the network model, along with an efficient network aware addressing scheme. This paper proposes a 24-bit hierarchical, application-confined unique naming technique (HAUNT-24), which ensures mutual distinctiveness of addresses within an application, with the primary focus on in-network data aggregation. Various issues relating to the proposed addressing scheme are analyzed and its future scope is discussed. The simple aggregate queries such as MIN/MAX and AVG are applied over the proposed HAUNT-24 addressed network and it's shown how it helps in achieving in-network data aggregation.