The utilization of advancing transformer-based deep neural network (DNN) models in edge environments holds the promise of improving productivity for intelligent tasks. However, deploying these models on edge devices with limited resources encounters significant performance challenges. Previous solutions have attempted to distribute computation tasks across devices and perform parallel inferences but often fall short of meeting service-level objectives (SLO). This limitation arises from their inability to effectively harness parallelization in transformer-based models and consider the resource diversity of edge devices. In this paper, we propose Hepti, a practical framework designed to facilitate parallel inference of transformer-based DNN models on heterogeneous edge environments. Hepti is armed with: 1) an understanding of transformer model architecture to enable effective parallel inference and 2) dynamic workload optimization to adapt to changing network and device resource capabilities. Our evaluations confirmed that the Hepti autonomously assesses the resource diversity of edge devices and network status. Furthermore, Hepti achieves a maximum performance improvement of 49.1% and 37.1% compared to the local inference approach and state-of-the-art model parallelisms on the BERT-Large model.

Key-Value SSD (KVSSD) has shown great potential for several important classes of emerging data stores due to its high throughput and low latency. When designing a key-value store with range queries, an LSM-tree is considered a better choice than a hash table due to its key ordering. However, the design space for range queries in LSM-tree-based KVSSDs has yet to be explored, despite range queries being one of the most demanding features. In this paper, we investigate the design constraints in LSM-tree-based KVSSDs from the perspective of range queries and propose three design principles. Based on these principles, we present IterKVSSD, an Iterator interface extended LSM-tree-based KVSSD for range queries. We implement IterKVSSD on OpenSSD Cosmos+, and our evaluation shows that it increases range query throughput by up to 4.13× and 7.22× for random and sequential key distributions, respectively, compared to existing KVSSDs.

Key-value SSDs (KVSSDs) introduced so far are limited in their use as an alternative to the key-value store running on the host due to the following technical limitations. First, they were designed only for a single tenant, limiting the use of multiple tenants. Second, they mainly focused on designing indexes for primary key-based searches, without supporting various queries using a combination of primary key and non-primary attribute-based searches. This article proposes Cerberus , a Log Structured Merged (LSM) tree-based KVSSD armed with (1) namespace and performance isolation for multiple tenants in a multi-tenant environment and (2) capability for processing non-primary attribute-based search queries. Specifically, Cerberus identifies the tenant’s namespace and splits a single large LSM-tree into namespace-specific LSM-tree indexes for tenants. Cerberus also manages secondary LSM-tree indexes to enable non-primary attribute-based data access and fast search query processing. With the SSD-internal CPU/DRAM resources, Cerberus supports non-primary attribute-based search queries and handles complex queries that are combined with search and computing operations. We prototyped Cerberus on the Cosmos+ OpenSSD platform. When there are multiple tenants, Cerberus exhibits up to 2.9× higher read throughput and negligible write overhead compared to existing KVSSD. Cerberus also shows lower latency by up to 9.31× for non-primary attribute-based queries.

Ransomware is a growing concern in business and government because it causes immediate financial damages or loss of important data. There is a way to detect and block ransomware in advance, but evolved ransomware can still attack while avoiding detection. Another alternative is to back up the original data. However, existing backup solutions can be under the control of ransomware and backup copies can be destroyed by ransomware. Moreover, backup methods incur storage and performance overhead. In this article, we propose AMOEBA, a device-level backup solution that does not require additional storage for backup. AMOEBA is armed with: 1) a hardware accelerator to run content-based detection algorithms for ransomware detection at high speed and 2) a fine-grained backup control mechanism to minimize space overhead for data backup. For evaluations, we not only implemented AMOEBA using the Microsoft solid-state drive (SSD) simulator but also prototyped it on the OpenSSD-platform. Our extensive evaluations with real ransomware workloads show that AMOEBA has high ransomware detection accuracy with negligible performance overhead.

Key-value SSDs (KVSSDs) implement the storage engine of a key-value store such as log-structured merge-tree (LSM-tree) inside the SSD. However, recent LSM-tree based KVSSDs cannot be used directly in a multi-tenant environment. LSMtree-based KVSSDs are not designed with isolation in mind in terms of namespaces and performance, leading to incorrect data access between concurrent users and poor read performance. In this paper, we propose Iso-KVSSD, a LSM-tree based KVSSD for multi-tenancy by supporting namespace and performance isolation. The Iso-KVSSD performs access control based on the user's namespace and constructs per-namespace dedicated LSM-trees for users. We implement the Iso-KVSSD on Cosmos+ OpenSSD in a Linux environment and evaluate performance with Put() and Get() workloads by varying the number of tenants. Our extensive evaluation results showed that Iso-KVSSD has negligible write performance overhead and an average 2.9 times higher read throughput than a baseline that manages one global shared LSM tree between users.

With the increasing importance of data, the threat of malware which destroys data has been increasing. If malware acquires the highest software privilege, any attempt to detect and remove malware can be disabled. In this paper, we propose DISKSHIELD, a secure storage framework. DISKSHIELD uses Intel SGX to provide Trusted Execution Environment (TEE) to the host, implements the file system into SSD firmware that provides a Trusted Computing Base (TCB), and uses a two-way authentication mechanism to securely transfer data from the host TEE to the SSD TCB against data tampering attacks. This design frees DISKSHIELD from attacks to the kernel. To show the efficacy of DISKSHIELD, we prototyped a DISKSHIELD system by modifying Intel IPFS and developing a device file system on the Jasmine OpenSSD Platform in a Linux environment. Our results show that DISKSHIELD provides strong data tamper resistance the throughput of read and write is on average to 28%, 19% lower than IPFS.